Abstract

X-ray fluorescence (XRF) tomography from nanoparticles (NPs) shows promise for high-spatial-resolution molecular imaging in small-animals. Quantitative reconstruction algorithms aim to reconstruct the true distribution of NPs inside the small-animal, but so far there has been no feasible way to predict signal levels or evaluate the accuracy of reconstructions in realistic scenarios. Here we present a GPU-based computational model for small-animal XRF tomography. The unique combination of a highly accelerated Monte Carlo tool combined with an accurate small-animal phantom allows unprecedented realistic full-body simulations. We use this model to simulate our experimental system to evaluate the quantitative performance and accuracy of our reconstruction algorithms on large-scale organs as well as mm-sized tumors. Furthermore, we predict the detection limits for sub-mm tumors at realistic NP concentrations. The computational model will be a valuable tool for optimizing next-generation experimental arrangements and reconstruction algorithms.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2018 (4)

J. C. Larsson, C. Vogt, W. Vågberg, M. S. Toprak, J. Dzieran, M. Arsenian-Henriksson, and H. M. Hertz, “High-spatial-resolution x-ray fluorescence tomography with spectrally matched nanoparticles,” Phys. Med. Biol. 63(16), 164001 (2018).
[Crossref] [PubMed]

C. A. S. Dunning and M. Bazalova-Carter, “Optimization of a table-top x-ray fluorescence computed tomography (XFCT) system,” Phys. Med. Biol. 63(23), 235013 (2018).
[Crossref] [PubMed]

C. A. S. Dunning and M. Bazalova-Carter, “Sheet beam x-ray fluorescence computed tomography (XFCT) imaging of gold nanoparticles,” Med. Phys. 45(6), 2572–2582 (2018).
[Crossref] [PubMed]

J. C. Larsson, K. Shaker, and H. M. Hertz, “Focused anti-scatter grid for background reduction in x-ray fluorescence tomography,” Opt. Lett. 43(11), 2591–2594 (2018).
[Crossref] [PubMed]

2017 (4)

Y. Zhou, M. Chen, H. Su, and J. Luo, “Self-prior strategy for organ reconstruction in fluorescence molecular tomography,” Biomed. Opt. Express 8(10), 4671–4686 (2017).
[Crossref] [PubMed]

Z. W. Di, S. Chen, Y. P. Hong, C. Jacobsen, S. Leyffer, and S. M. Wild, “Joint reconstruction of x-ray fluorescence and transmission tomography,” Opt. Express 25(12), 13107–13124 (2017).
[Crossref] [PubMed]

T. Sasaya, N. Sunaguchi, K. Hyodo, T. Zeniya, and T. Yuasa, “Multi-pinhole fluorescent x-ray computed tomography for molecular imaging,” Sci. Rep. 7(1), 5742 (2017).
[Crossref] [PubMed]

L. Li, S. Zhang, R. Li, and Z. Chen, “Full-field fan-beam x-ray fluorescence computed tomography with a conventional x-ray tube and photon-counting detectors for fast nanoparticle bioimaging,” Opt. Eng. 56(4), 043106 (2017).
[Crossref]

2016 (2)

N. Manohar, F. J. Reynoso, P. Diagaradjane, S. Krishnan, and S. H. Cho, “Quantitative imaging of gold nanoparticle distribution in a tumor-bearing mouse using benchtop x-ray fluorescence computed tomography,” Sci. Rep. 6(1), 22079 (2016).
[Crossref] [PubMed]

Y. Wang, T. R. Mazur, O. Green, Y. Hu, H. Li, V. Rodriguez, H. O. Wooten, D. Yang, T. Zhao, S. Mutic, and H. H. Li, “A GPU-accelerated Monte Carlo dose calculation platform and its application toward validating an MRI-guided radiation therapy beam model,” Med. Phys. 43(7), 4040–4052 (2016).
[Crossref] [PubMed]

2015 (1)

M. Ahmad, M. Bazalova-Carter, R. Fahrig, and L. Xing, “Optimized detector angular configuration increases the sensitivity of x-ray fluorescence computed tomography (XFCT),” IEEE Trans. Med. Imaging 34(5), 1140–1147 (2015).
[Crossref] [PubMed]

2014 (3)

H. M. Hertz, J. C. Larsson, U. Lundström, D. H. Larsson, and C. Vogt, “Laboratory x-ray fluorescence tomography for high-resolution nanoparticle bio-imaging,” Opt. Lett. 39(9), 2790–2793 (2014).
[Crossref] [PubMed]

M. Sjölin and M. Danielsson, “Improved signal-to-noise ratio for non-perpendicular detection angles in x-ray fluorescence computed tomography (XFCT),” Phys. Med. Biol. 59(21), 6507–6520 (2014).
[Crossref] [PubMed]

Q. Yang, B. Deng, G. Du, H. Xie, G. Zhou, T. Xiao, and H. Xu, “X‐ray fluorescence computed tomography with absorption correction for biomedical samples,” XRay Spectrom. 43(5), 278–285 (2014).
[Crossref]

2013 (1)

Y. Kuang, G. Pratx, M. Bazalova, B. Meng, J. Qian, and L. Xing, “First demonstration of multiplexed X-ray fluorescence computed tomography (XFCT) imaging,” IEEE Trans. Med. Imaging 32(2), 262–267 (2013).
[Crossref] [PubMed]

2012 (2)

B. L. Jones, N. Manohar, F. Reynoso, A. Karellas, and S. H. Cho, “Experimental demonstration of benchtop x-ray fluorescence computed tomography XFCT of gold nanoparticle-loaded objects using lead- and tin-filtered polychromatic cone-beams,” Phys. Med. Biol. 57, N457–N467 (2012).

M. Bazalova, Y. Kuang, G. Pratx, and L. Xing, “Investigation of X-ray fluorescence computed tomography (XFCT) and K-edge imaging,” IEEE Trans. Med. Imaging 31(8), 1620–1627 (2012).
[Crossref] [PubMed]

2011 (2)

B. L. Jones and S. H. Cho, “The feasibility of polychromatic cone-beam x-ray fluorescence computed tomography (XFCT) imaging of gold nanoparticle-loaded objects: a Monte Carlo study,” Phys. Med. Biol. 56(12), 3719–3730 (2011).
[Crossref] [PubMed]

J. F. Hainfeld, M. J. O’Connor, F. A. Dilmanian, D. N. Slatkin, D. J. Adams, and H. M. Smilowitz, “Micro-CT enables microlocalisation and quantification of Her2-targeted gold nanoparticles within tumour regions,” Br. J. Radiol. 84(1002), 526–533 (2011).
[Crossref] [PubMed]

2010 (2)

S. K. Cheong, B. L. Jones, A. K. Siddiqi, F. Liu, N. Manohar, and S. H. Cho, “X-ray fluorescence computed tomography (XFCT) imaging of gold nanoparticle-loaded objects using 110 kVp x-rays,” Phys. Med. Biol. 55(3), 647–662 (2010).
[Crossref] [PubMed]

X. Jia, X. Gu, J. Sempau, D. Choi, A. Majumdar, and S. B. Jiang, “Development of a GPU-based Monte Carlo dose calculation code for coupled electron-photon transport,” Phys. Med. Biol. 55(11), 3077–3086 (2010).
[Crossref] [PubMed]

2009 (1)

T. Takeda, J. Wu, Q. Huo, T. Yuasa, K. Hyodo, F. A. Dilmanian, and T. Akatsuka, “X-ray fluorescent CT imaging of cerebral uptake of stable-iodine perfusion agent iodoamphetamine analog IMP in mice,” J. Synchrotron Radiat. 16(1), 57–62 (2009).
[Crossref] [PubMed]

2008 (1)

C. T. Badea, M. Drangova, D. W. Holdsworth, and G. A. Johnson, “In vivo small-animal imaging using micro-CT and digital subtraction angiography,” Phys. Med. Biol. 53(19), R319–R350 (2008).
[Crossref] [PubMed]

2007 (1)

B. Dogdas, D. Stout, A. F. Chatziioannou, and R. M. Leahy, “Digimouse: a 3D whole body mouse atlas from CT and cryosection data,” Phys. Med. Biol. 52(3), 577–587 (2007).
[Crossref] [PubMed]

2004 (1)

J. F. Hainfeld, D. N. Slatkin, and H. M. Smilowitz, “The use of gold nanoparticles to enhance radiotherapy in mice,” Phys. Med. Biol. 49(18), N309–N315 (2004).
[Crossref] [PubMed]

2001 (1)

A. Brunetti and B. Golosio, “Software for X-ray fluorescence and scattering tomographic reconstruction,” Comput. Phys. Commun. 141(3), 412–425 (2001).
[Crossref]

2000 (1)

A. Simionovici, M. Chukalina, C. Schroer, M. Drakopoulos, A. Snigirev, I. Snigireva, B. Lengeler, K. Janssens, and F. Adams, “High-resolution x-ray fluorescence microtomography of homogeneous samples,” IEEE Trans. Nucl. Sci. 47(6), 2736–2740 (2000).
[Crossref]

1991 (1)

J. P. Hogan, R. A. Gonsalves, and A. S. Krieger, “Fluorescent computer tomography: a model for correction of X-ray absorption,” IEEE Trans. Nucl. Sci. 38(6), 1721–1727 (1991).
[Crossref]

1948 (1)

Adams, D. J.

J. F. Hainfeld, M. J. O’Connor, F. A. Dilmanian, D. N. Slatkin, D. J. Adams, and H. M. Smilowitz, “Micro-CT enables microlocalisation and quantification of Her2-targeted gold nanoparticles within tumour regions,” Br. J. Radiol. 84(1002), 526–533 (2011).
[Crossref] [PubMed]

Adams, F.

A. Simionovici, M. Chukalina, C. Schroer, M. Drakopoulos, A. Snigirev, I. Snigireva, B. Lengeler, K. Janssens, and F. Adams, “High-resolution x-ray fluorescence microtomography of homogeneous samples,” IEEE Trans. Nucl. Sci. 47(6), 2736–2740 (2000).
[Crossref]

Ahmad, M.

M. Ahmad, M. Bazalova-Carter, R. Fahrig, and L. Xing, “Optimized detector angular configuration increases the sensitivity of x-ray fluorescence computed tomography (XFCT),” IEEE Trans. Med. Imaging 34(5), 1140–1147 (2015).
[Crossref] [PubMed]

Ahmed, M. F.

M. F. Ahmed, S. Jayarathna, and S. H. Cho, “Feasibility of X-ray Fluorescence Computed Tomography (XFCT) Imaging of Human Lung Tumors loaded with Gold Nanoparticles: A Monte Carlo Study,” in Proceedings of IEEE 12th International Conference on Nano/Molecular Medicine and Engineering (IEEE, 2018), pp. 250–254.
[Crossref]

Akatsuka, T.

T. Takeda, J. Wu, Q. Huo, T. Yuasa, K. Hyodo, F. A. Dilmanian, and T. Akatsuka, “X-ray fluorescent CT imaging of cerebral uptake of stable-iodine perfusion agent iodoamphetamine analog IMP in mice,” J. Synchrotron Radiat. 16(1), 57–62 (2009).
[Crossref] [PubMed]

Arsenian-Henriksson, M.

J. C. Larsson, C. Vogt, W. Vågberg, M. S. Toprak, J. Dzieran, M. Arsenian-Henriksson, and H. M. Hertz, “High-spatial-resolution x-ray fluorescence tomography with spectrally matched nanoparticles,” Phys. Med. Biol. 63(16), 164001 (2018).
[Crossref] [PubMed]

Badal, A.

A. Badal and A. Badano, “Monte Carlo simulation of X-ray imaging using a graphics processing unit,” in Proceedings of IEEE Nuclear Science Symposium Conference Record (NSS/MIC) (IEEE, 2009), pp. 4081–4084.
[Crossref]

Badano, A.

A. Badal and A. Badano, “Monte Carlo simulation of X-ray imaging using a graphics processing unit,” in Proceedings of IEEE Nuclear Science Symposium Conference Record (NSS/MIC) (IEEE, 2009), pp. 4081–4084.
[Crossref]

Badea, C. T.

C. T. Badea, M. Drangova, D. W. Holdsworth, and G. A. Johnson, “In vivo small-animal imaging using micro-CT and digital subtraction angiography,” Phys. Med. Biol. 53(19), R319–R350 (2008).
[Crossref] [PubMed]

Bazalova, M.

Y. Kuang, G. Pratx, M. Bazalova, B. Meng, J. Qian, and L. Xing, “First demonstration of multiplexed X-ray fluorescence computed tomography (XFCT) imaging,” IEEE Trans. Med. Imaging 32(2), 262–267 (2013).
[Crossref] [PubMed]

M. Bazalova, Y. Kuang, G. Pratx, and L. Xing, “Investigation of X-ray fluorescence computed tomography (XFCT) and K-edge imaging,” IEEE Trans. Med. Imaging 31(8), 1620–1627 (2012).
[Crossref] [PubMed]

Bazalova-Carter, M.

C. A. S. Dunning and M. Bazalova-Carter, “Optimization of a table-top x-ray fluorescence computed tomography (XFCT) system,” Phys. Med. Biol. 63(23), 235013 (2018).
[Crossref] [PubMed]

C. A. S. Dunning and M. Bazalova-Carter, “Sheet beam x-ray fluorescence computed tomography (XFCT) imaging of gold nanoparticles,” Med. Phys. 45(6), 2572–2582 (2018).
[Crossref] [PubMed]

M. Ahmad, M. Bazalova-Carter, R. Fahrig, and L. Xing, “Optimized detector angular configuration increases the sensitivity of x-ray fluorescence computed tomography (XFCT),” IEEE Trans. Med. Imaging 34(5), 1140–1147 (2015).
[Crossref] [PubMed]

Brunetti, A.

A. Brunetti and B. Golosio, “Software for X-ray fluorescence and scattering tomographic reconstruction,” Comput. Phys. Commun. 141(3), 412–425 (2001).
[Crossref]

Chatziioannou, A. F.

B. Dogdas, D. Stout, A. F. Chatziioannou, and R. M. Leahy, “Digimouse: a 3D whole body mouse atlas from CT and cryosection data,” Phys. Med. Biol. 52(3), 577–587 (2007).
[Crossref] [PubMed]

Chen, M.

Chen, S.

Chen, Z.

L. Li, S. Zhang, R. Li, and Z. Chen, “Full-field fan-beam x-ray fluorescence computed tomography with a conventional x-ray tube and photon-counting detectors for fast nanoparticle bioimaging,” Opt. Eng. 56(4), 043106 (2017).
[Crossref]

Cheong, S. K.

S. K. Cheong, B. L. Jones, A. K. Siddiqi, F. Liu, N. Manohar, and S. H. Cho, “X-ray fluorescence computed tomography (XFCT) imaging of gold nanoparticle-loaded objects using 110 kVp x-rays,” Phys. Med. Biol. 55(3), 647–662 (2010).
[Crossref] [PubMed]

Cho, S. H.

N. Manohar, F. J. Reynoso, P. Diagaradjane, S. Krishnan, and S. H. Cho, “Quantitative imaging of gold nanoparticle distribution in a tumor-bearing mouse using benchtop x-ray fluorescence computed tomography,” Sci. Rep. 6(1), 22079 (2016).
[Crossref] [PubMed]

B. L. Jones, N. Manohar, F. Reynoso, A. Karellas, and S. H. Cho, “Experimental demonstration of benchtop x-ray fluorescence computed tomography XFCT of gold nanoparticle-loaded objects using lead- and tin-filtered polychromatic cone-beams,” Phys. Med. Biol. 57, N457–N467 (2012).

B. L. Jones and S. H. Cho, “The feasibility of polychromatic cone-beam x-ray fluorescence computed tomography (XFCT) imaging of gold nanoparticle-loaded objects: a Monte Carlo study,” Phys. Med. Biol. 56(12), 3719–3730 (2011).
[Crossref] [PubMed]

S. K. Cheong, B. L. Jones, A. K. Siddiqi, F. Liu, N. Manohar, and S. H. Cho, “X-ray fluorescence computed tomography (XFCT) imaging of gold nanoparticle-loaded objects using 110 kVp x-rays,” Phys. Med. Biol. 55(3), 647–662 (2010).
[Crossref] [PubMed]

M. F. Ahmed, S. Jayarathna, and S. H. Cho, “Feasibility of X-ray Fluorescence Computed Tomography (XFCT) Imaging of Human Lung Tumors loaded with Gold Nanoparticles: A Monte Carlo Study,” in Proceedings of IEEE 12th International Conference on Nano/Molecular Medicine and Engineering (IEEE, 2018), pp. 250–254.
[Crossref]

Choi, D.

X. Jia, X. Gu, J. Sempau, D. Choi, A. Majumdar, and S. B. Jiang, “Development of a GPU-based Monte Carlo dose calculation code for coupled electron-photon transport,” Phys. Med. Biol. 55(11), 3077–3086 (2010).
[Crossref] [PubMed]

Chukalina, M.

A. Simionovici, M. Chukalina, C. Schroer, M. Drakopoulos, A. Snigirev, I. Snigireva, B. Lengeler, K. Janssens, and F. Adams, “High-resolution x-ray fluorescence microtomography of homogeneous samples,” IEEE Trans. Nucl. Sci. 47(6), 2736–2740 (2000).
[Crossref]

Danielsson, M.

M. Sjölin and M. Danielsson, “Improved signal-to-noise ratio for non-perpendicular detection angles in x-ray fluorescence computed tomography (XFCT),” Phys. Med. Biol. 59(21), 6507–6520 (2014).
[Crossref] [PubMed]

Deng, B.

Q. Yang, B. Deng, G. Du, H. Xie, G. Zhou, T. Xiao, and H. Xu, “X‐ray fluorescence computed tomography with absorption correction for biomedical samples,” XRay Spectrom. 43(5), 278–285 (2014).
[Crossref]

Di, Z. W.

Diagaradjane, P.

N. Manohar, F. J. Reynoso, P. Diagaradjane, S. Krishnan, and S. H. Cho, “Quantitative imaging of gold nanoparticle distribution in a tumor-bearing mouse using benchtop x-ray fluorescence computed tomography,” Sci. Rep. 6(1), 22079 (2016).
[Crossref] [PubMed]

Dilmanian, F. A.

J. F. Hainfeld, M. J. O’Connor, F. A. Dilmanian, D. N. Slatkin, D. J. Adams, and H. M. Smilowitz, “Micro-CT enables microlocalisation and quantification of Her2-targeted gold nanoparticles within tumour regions,” Br. J. Radiol. 84(1002), 526–533 (2011).
[Crossref] [PubMed]

T. Takeda, J. Wu, Q. Huo, T. Yuasa, K. Hyodo, F. A. Dilmanian, and T. Akatsuka, “X-ray fluorescent CT imaging of cerebral uptake of stable-iodine perfusion agent iodoamphetamine analog IMP in mice,” J. Synchrotron Radiat. 16(1), 57–62 (2009).
[Crossref] [PubMed]

Dogdas, B.

B. Dogdas, D. Stout, A. F. Chatziioannou, and R. M. Leahy, “Digimouse: a 3D whole body mouse atlas from CT and cryosection data,” Phys. Med. Biol. 52(3), 577–587 (2007).
[Crossref] [PubMed]

Drakopoulos, M.

A. Simionovici, M. Chukalina, C. Schroer, M. Drakopoulos, A. Snigirev, I. Snigireva, B. Lengeler, K. Janssens, and F. Adams, “High-resolution x-ray fluorescence microtomography of homogeneous samples,” IEEE Trans. Nucl. Sci. 47(6), 2736–2740 (2000).
[Crossref]

Drangova, M.

C. T. Badea, M. Drangova, D. W. Holdsworth, and G. A. Johnson, “In vivo small-animal imaging using micro-CT and digital subtraction angiography,” Phys. Med. Biol. 53(19), R319–R350 (2008).
[Crossref] [PubMed]

Du, G.

Q. Yang, B. Deng, G. Du, H. Xie, G. Zhou, T. Xiao, and H. Xu, “X‐ray fluorescence computed tomography with absorption correction for biomedical samples,” XRay Spectrom. 43(5), 278–285 (2014).
[Crossref]

Dunning, C. A. S.

C. A. S. Dunning and M. Bazalova-Carter, “Sheet beam x-ray fluorescence computed tomography (XFCT) imaging of gold nanoparticles,” Med. Phys. 45(6), 2572–2582 (2018).
[Crossref] [PubMed]

C. A. S. Dunning and M. Bazalova-Carter, “Optimization of a table-top x-ray fluorescence computed tomography (XFCT) system,” Phys. Med. Biol. 63(23), 235013 (2018).
[Crossref] [PubMed]

Dzieran, J.

J. C. Larsson, C. Vogt, W. Vågberg, M. S. Toprak, J. Dzieran, M. Arsenian-Henriksson, and H. M. Hertz, “High-spatial-resolution x-ray fluorescence tomography with spectrally matched nanoparticles,” Phys. Med. Biol. 63(16), 164001 (2018).
[Crossref] [PubMed]

Fahrig, R.

M. Ahmad, M. Bazalova-Carter, R. Fahrig, and L. Xing, “Optimized detector angular configuration increases the sensitivity of x-ray fluorescence computed tomography (XFCT),” IEEE Trans. Med. Imaging 34(5), 1140–1147 (2015).
[Crossref] [PubMed]

Golosio, B.

A. Brunetti and B. Golosio, “Software for X-ray fluorescence and scattering tomographic reconstruction,” Comput. Phys. Commun. 141(3), 412–425 (2001).
[Crossref]

Gonsalves, R. A.

J. P. Hogan, R. A. Gonsalves, and A. S. Krieger, “Fluorescent computer tomography: a model for correction of X-ray absorption,” IEEE Trans. Nucl. Sci. 38(6), 1721–1727 (1991).
[Crossref]

Green, O.

Y. Wang, T. R. Mazur, O. Green, Y. Hu, H. Li, V. Rodriguez, H. O. Wooten, D. Yang, T. Zhao, S. Mutic, and H. H. Li, “A GPU-accelerated Monte Carlo dose calculation platform and its application toward validating an MRI-guided radiation therapy beam model,” Med. Phys. 43(7), 4040–4052 (2016).
[Crossref] [PubMed]

Gu, X.

X. Jia, X. Gu, J. Sempau, D. Choi, A. Majumdar, and S. B. Jiang, “Development of a GPU-based Monte Carlo dose calculation code for coupled electron-photon transport,” Phys. Med. Biol. 55(11), 3077–3086 (2010).
[Crossref] [PubMed]

Hainfeld, J. F.

J. F. Hainfeld, M. J. O’Connor, F. A. Dilmanian, D. N. Slatkin, D. J. Adams, and H. M. Smilowitz, “Micro-CT enables microlocalisation and quantification of Her2-targeted gold nanoparticles within tumour regions,” Br. J. Radiol. 84(1002), 526–533 (2011).
[Crossref] [PubMed]

J. F. Hainfeld, D. N. Slatkin, and H. M. Smilowitz, “The use of gold nanoparticles to enhance radiotherapy in mice,” Phys. Med. Biol. 49(18), N309–N315 (2004).
[Crossref] [PubMed]

Hertz, H. M.

Hogan, J. P.

J. P. Hogan, R. A. Gonsalves, and A. S. Krieger, “Fluorescent computer tomography: a model for correction of X-ray absorption,” IEEE Trans. Nucl. Sci. 38(6), 1721–1727 (1991).
[Crossref]

Holdsworth, D. W.

C. T. Badea, M. Drangova, D. W. Holdsworth, and G. A. Johnson, “In vivo small-animal imaging using micro-CT and digital subtraction angiography,” Phys. Med. Biol. 53(19), R319–R350 (2008).
[Crossref] [PubMed]

Hong, Y. P.

Hu, Y.

Y. Wang, T. R. Mazur, O. Green, Y. Hu, H. Li, V. Rodriguez, H. O. Wooten, D. Yang, T. Zhao, S. Mutic, and H. H. Li, “A GPU-accelerated Monte Carlo dose calculation platform and its application toward validating an MRI-guided radiation therapy beam model,” Med. Phys. 43(7), 4040–4052 (2016).
[Crossref] [PubMed]

Huo, Q.

T. Takeda, J. Wu, Q. Huo, T. Yuasa, K. Hyodo, F. A. Dilmanian, and T. Akatsuka, “X-ray fluorescent CT imaging of cerebral uptake of stable-iodine perfusion agent iodoamphetamine analog IMP in mice,” J. Synchrotron Radiat. 16(1), 57–62 (2009).
[Crossref] [PubMed]

Hyodo, K.

T. Sasaya, N. Sunaguchi, K. Hyodo, T. Zeniya, and T. Yuasa, “Multi-pinhole fluorescent x-ray computed tomography for molecular imaging,” Sci. Rep. 7(1), 5742 (2017).
[Crossref] [PubMed]

T. Takeda, J. Wu, Q. Huo, T. Yuasa, K. Hyodo, F. A. Dilmanian, and T. Akatsuka, “X-ray fluorescent CT imaging of cerebral uptake of stable-iodine perfusion agent iodoamphetamine analog IMP in mice,” J. Synchrotron Radiat. 16(1), 57–62 (2009).
[Crossref] [PubMed]

Jacobsen, C.

Janssens, K.

A. Simionovici, M. Chukalina, C. Schroer, M. Drakopoulos, A. Snigirev, I. Snigireva, B. Lengeler, K. Janssens, and F. Adams, “High-resolution x-ray fluorescence microtomography of homogeneous samples,” IEEE Trans. Nucl. Sci. 47(6), 2736–2740 (2000).
[Crossref]

Jayarathna, S.

M. F. Ahmed, S. Jayarathna, and S. H. Cho, “Feasibility of X-ray Fluorescence Computed Tomography (XFCT) Imaging of Human Lung Tumors loaded with Gold Nanoparticles: A Monte Carlo Study,” in Proceedings of IEEE 12th International Conference on Nano/Molecular Medicine and Engineering (IEEE, 2018), pp. 250–254.
[Crossref]

Jia, X.

X. Jia, X. Gu, J. Sempau, D. Choi, A. Majumdar, and S. B. Jiang, “Development of a GPU-based Monte Carlo dose calculation code for coupled electron-photon transport,” Phys. Med. Biol. 55(11), 3077–3086 (2010).
[Crossref] [PubMed]

Jiang, S. B.

X. Jia, X. Gu, J. Sempau, D. Choi, A. Majumdar, and S. B. Jiang, “Development of a GPU-based Monte Carlo dose calculation code for coupled electron-photon transport,” Phys. Med. Biol. 55(11), 3077–3086 (2010).
[Crossref] [PubMed]

Johnson, G. A.

C. T. Badea, M. Drangova, D. W. Holdsworth, and G. A. Johnson, “In vivo small-animal imaging using micro-CT and digital subtraction angiography,” Phys. Med. Biol. 53(19), R319–R350 (2008).
[Crossref] [PubMed]

Jones, B. L.

B. L. Jones, N. Manohar, F. Reynoso, A. Karellas, and S. H. Cho, “Experimental demonstration of benchtop x-ray fluorescence computed tomography XFCT of gold nanoparticle-loaded objects using lead- and tin-filtered polychromatic cone-beams,” Phys. Med. Biol. 57, N457–N467 (2012).

B. L. Jones and S. H. Cho, “The feasibility of polychromatic cone-beam x-ray fluorescence computed tomography (XFCT) imaging of gold nanoparticle-loaded objects: a Monte Carlo study,” Phys. Med. Biol. 56(12), 3719–3730 (2011).
[Crossref] [PubMed]

S. K. Cheong, B. L. Jones, A. K. Siddiqi, F. Liu, N. Manohar, and S. H. Cho, “X-ray fluorescence computed tomography (XFCT) imaging of gold nanoparticle-loaded objects using 110 kVp x-rays,” Phys. Med. Biol. 55(3), 647–662 (2010).
[Crossref] [PubMed]

Karellas, A.

B. L. Jones, N. Manohar, F. Reynoso, A. Karellas, and S. H. Cho, “Experimental demonstration of benchtop x-ray fluorescence computed tomography XFCT of gold nanoparticle-loaded objects using lead- and tin-filtered polychromatic cone-beams,” Phys. Med. Biol. 57, N457–N467 (2012).

Krieger, A. S.

J. P. Hogan, R. A. Gonsalves, and A. S. Krieger, “Fluorescent computer tomography: a model for correction of X-ray absorption,” IEEE Trans. Nucl. Sci. 38(6), 1721–1727 (1991).
[Crossref]

Krishnan, S.

N. Manohar, F. J. Reynoso, P. Diagaradjane, S. Krishnan, and S. H. Cho, “Quantitative imaging of gold nanoparticle distribution in a tumor-bearing mouse using benchtop x-ray fluorescence computed tomography,” Sci. Rep. 6(1), 22079 (2016).
[Crossref] [PubMed]

Kuang, Y.

Y. Kuang, G. Pratx, M. Bazalova, B. Meng, J. Qian, and L. Xing, “First demonstration of multiplexed X-ray fluorescence computed tomography (XFCT) imaging,” IEEE Trans. Med. Imaging 32(2), 262–267 (2013).
[Crossref] [PubMed]

M. Bazalova, Y. Kuang, G. Pratx, and L. Xing, “Investigation of X-ray fluorescence computed tomography (XFCT) and K-edge imaging,” IEEE Trans. Med. Imaging 31(8), 1620–1627 (2012).
[Crossref] [PubMed]

Larsson, D. H.

Larsson, J. C.

Leahy, R. M.

B. Dogdas, D. Stout, A. F. Chatziioannou, and R. M. Leahy, “Digimouse: a 3D whole body mouse atlas from CT and cryosection data,” Phys. Med. Biol. 52(3), 577–587 (2007).
[Crossref] [PubMed]

Lengeler, B.

A. Simionovici, M. Chukalina, C. Schroer, M. Drakopoulos, A. Snigirev, I. Snigireva, B. Lengeler, K. Janssens, and F. Adams, “High-resolution x-ray fluorescence microtomography of homogeneous samples,” IEEE Trans. Nucl. Sci. 47(6), 2736–2740 (2000).
[Crossref]

Leyffer, S.

Li, H.

Y. Wang, T. R. Mazur, O. Green, Y. Hu, H. Li, V. Rodriguez, H. O. Wooten, D. Yang, T. Zhao, S. Mutic, and H. H. Li, “A GPU-accelerated Monte Carlo dose calculation platform and its application toward validating an MRI-guided radiation therapy beam model,” Med. Phys. 43(7), 4040–4052 (2016).
[Crossref] [PubMed]

Li, H. H.

Y. Wang, T. R. Mazur, O. Green, Y. Hu, H. Li, V. Rodriguez, H. O. Wooten, D. Yang, T. Zhao, S. Mutic, and H. H. Li, “A GPU-accelerated Monte Carlo dose calculation platform and its application toward validating an MRI-guided radiation therapy beam model,” Med. Phys. 43(7), 4040–4052 (2016).
[Crossref] [PubMed]

Li, L.

L. Li, S. Zhang, R. Li, and Z. Chen, “Full-field fan-beam x-ray fluorescence computed tomography with a conventional x-ray tube and photon-counting detectors for fast nanoparticle bioimaging,” Opt. Eng. 56(4), 043106 (2017).
[Crossref]

Li, R.

L. Li, S. Zhang, R. Li, and Z. Chen, “Full-field fan-beam x-ray fluorescence computed tomography with a conventional x-ray tube and photon-counting detectors for fast nanoparticle bioimaging,” Opt. Eng. 56(4), 043106 (2017).
[Crossref]

Liu, F.

S. K. Cheong, B. L. Jones, A. K. Siddiqi, F. Liu, N. Manohar, and S. H. Cho, “X-ray fluorescence computed tomography (XFCT) imaging of gold nanoparticle-loaded objects using 110 kVp x-rays,” Phys. Med. Biol. 55(3), 647–662 (2010).
[Crossref] [PubMed]

Lundström, U.

Luo, J.

Majumdar, A.

X. Jia, X. Gu, J. Sempau, D. Choi, A. Majumdar, and S. B. Jiang, “Development of a GPU-based Monte Carlo dose calculation code for coupled electron-photon transport,” Phys. Med. Biol. 55(11), 3077–3086 (2010).
[Crossref] [PubMed]

Manohar, N.

N. Manohar, F. J. Reynoso, P. Diagaradjane, S. Krishnan, and S. H. Cho, “Quantitative imaging of gold nanoparticle distribution in a tumor-bearing mouse using benchtop x-ray fluorescence computed tomography,” Sci. Rep. 6(1), 22079 (2016).
[Crossref] [PubMed]

B. L. Jones, N. Manohar, F. Reynoso, A. Karellas, and S. H. Cho, “Experimental demonstration of benchtop x-ray fluorescence computed tomography XFCT of gold nanoparticle-loaded objects using lead- and tin-filtered polychromatic cone-beams,” Phys. Med. Biol. 57, N457–N467 (2012).

S. K. Cheong, B. L. Jones, A. K. Siddiqi, F. Liu, N. Manohar, and S. H. Cho, “X-ray fluorescence computed tomography (XFCT) imaging of gold nanoparticle-loaded objects using 110 kVp x-rays,” Phys. Med. Biol. 55(3), 647–662 (2010).
[Crossref] [PubMed]

Mazur, T. R.

Y. Wang, T. R. Mazur, O. Green, Y. Hu, H. Li, V. Rodriguez, H. O. Wooten, D. Yang, T. Zhao, S. Mutic, and H. H. Li, “A GPU-accelerated Monte Carlo dose calculation platform and its application toward validating an MRI-guided radiation therapy beam model,” Med. Phys. 43(7), 4040–4052 (2016).
[Crossref] [PubMed]

Meng, B.

Y. Kuang, G. Pratx, M. Bazalova, B. Meng, J. Qian, and L. Xing, “First demonstration of multiplexed X-ray fluorescence computed tomography (XFCT) imaging,” IEEE Trans. Med. Imaging 32(2), 262–267 (2013).
[Crossref] [PubMed]

Mutic, S.

Y. Wang, T. R. Mazur, O. Green, Y. Hu, H. Li, V. Rodriguez, H. O. Wooten, D. Yang, T. Zhao, S. Mutic, and H. H. Li, “A GPU-accelerated Monte Carlo dose calculation platform and its application toward validating an MRI-guided radiation therapy beam model,” Med. Phys. 43(7), 4040–4052 (2016).
[Crossref] [PubMed]

O’Connor, M. J.

J. F. Hainfeld, M. J. O’Connor, F. A. Dilmanian, D. N. Slatkin, D. J. Adams, and H. M. Smilowitz, “Micro-CT enables microlocalisation and quantification of Her2-targeted gold nanoparticles within tumour regions,” Br. J. Radiol. 84(1002), 526–533 (2011).
[Crossref] [PubMed]

Pratx, G.

Y. Kuang, G. Pratx, M. Bazalova, B. Meng, J. Qian, and L. Xing, “First demonstration of multiplexed X-ray fluorescence computed tomography (XFCT) imaging,” IEEE Trans. Med. Imaging 32(2), 262–267 (2013).
[Crossref] [PubMed]

M. Bazalova, Y. Kuang, G. Pratx, and L. Xing, “Investigation of X-ray fluorescence computed tomography (XFCT) and K-edge imaging,” IEEE Trans. Med. Imaging 31(8), 1620–1627 (2012).
[Crossref] [PubMed]

Qian, J.

Y. Kuang, G. Pratx, M. Bazalova, B. Meng, J. Qian, and L. Xing, “First demonstration of multiplexed X-ray fluorescence computed tomography (XFCT) imaging,” IEEE Trans. Med. Imaging 32(2), 262–267 (2013).
[Crossref] [PubMed]

Reynoso, F.

B. L. Jones, N. Manohar, F. Reynoso, A. Karellas, and S. H. Cho, “Experimental demonstration of benchtop x-ray fluorescence computed tomography XFCT of gold nanoparticle-loaded objects using lead- and tin-filtered polychromatic cone-beams,” Phys. Med. Biol. 57, N457–N467 (2012).

Reynoso, F. J.

N. Manohar, F. J. Reynoso, P. Diagaradjane, S. Krishnan, and S. H. Cho, “Quantitative imaging of gold nanoparticle distribution in a tumor-bearing mouse using benchtop x-ray fluorescence computed tomography,” Sci. Rep. 6(1), 22079 (2016).
[Crossref] [PubMed]

Rodriguez, V.

Y. Wang, T. R. Mazur, O. Green, Y. Hu, H. Li, V. Rodriguez, H. O. Wooten, D. Yang, T. Zhao, S. Mutic, and H. H. Li, “A GPU-accelerated Monte Carlo dose calculation platform and its application toward validating an MRI-guided radiation therapy beam model,” Med. Phys. 43(7), 4040–4052 (2016).
[Crossref] [PubMed]

Rose, A.

Sasaya, T.

T. Sasaya, N. Sunaguchi, K. Hyodo, T. Zeniya, and T. Yuasa, “Multi-pinhole fluorescent x-ray computed tomography for molecular imaging,” Sci. Rep. 7(1), 5742 (2017).
[Crossref] [PubMed]

Schroer, C.

A. Simionovici, M. Chukalina, C. Schroer, M. Drakopoulos, A. Snigirev, I. Snigireva, B. Lengeler, K. Janssens, and F. Adams, “High-resolution x-ray fluorescence microtomography of homogeneous samples,” IEEE Trans. Nucl. Sci. 47(6), 2736–2740 (2000).
[Crossref]

Sempau, J.

X. Jia, X. Gu, J. Sempau, D. Choi, A. Majumdar, and S. B. Jiang, “Development of a GPU-based Monte Carlo dose calculation code for coupled electron-photon transport,” Phys. Med. Biol. 55(11), 3077–3086 (2010).
[Crossref] [PubMed]

Shaker, K.

Siddiqi, A. K.

S. K. Cheong, B. L. Jones, A. K. Siddiqi, F. Liu, N. Manohar, and S. H. Cho, “X-ray fluorescence computed tomography (XFCT) imaging of gold nanoparticle-loaded objects using 110 kVp x-rays,” Phys. Med. Biol. 55(3), 647–662 (2010).
[Crossref] [PubMed]

Simionovici, A.

A. Simionovici, M. Chukalina, C. Schroer, M. Drakopoulos, A. Snigirev, I. Snigireva, B. Lengeler, K. Janssens, and F. Adams, “High-resolution x-ray fluorescence microtomography of homogeneous samples,” IEEE Trans. Nucl. Sci. 47(6), 2736–2740 (2000).
[Crossref]

Sjölin, M.

M. Sjölin and M. Danielsson, “Improved signal-to-noise ratio for non-perpendicular detection angles in x-ray fluorescence computed tomography (XFCT),” Phys. Med. Biol. 59(21), 6507–6520 (2014).
[Crossref] [PubMed]

Slatkin, D. N.

J. F. Hainfeld, M. J. O’Connor, F. A. Dilmanian, D. N. Slatkin, D. J. Adams, and H. M. Smilowitz, “Micro-CT enables microlocalisation and quantification of Her2-targeted gold nanoparticles within tumour regions,” Br. J. Radiol. 84(1002), 526–533 (2011).
[Crossref] [PubMed]

J. F. Hainfeld, D. N. Slatkin, and H. M. Smilowitz, “The use of gold nanoparticles to enhance radiotherapy in mice,” Phys. Med. Biol. 49(18), N309–N315 (2004).
[Crossref] [PubMed]

Smilowitz, H. M.

J. F. Hainfeld, M. J. O’Connor, F. A. Dilmanian, D. N. Slatkin, D. J. Adams, and H. M. Smilowitz, “Micro-CT enables microlocalisation and quantification of Her2-targeted gold nanoparticles within tumour regions,” Br. J. Radiol. 84(1002), 526–533 (2011).
[Crossref] [PubMed]

J. F. Hainfeld, D. N. Slatkin, and H. M. Smilowitz, “The use of gold nanoparticles to enhance radiotherapy in mice,” Phys. Med. Biol. 49(18), N309–N315 (2004).
[Crossref] [PubMed]

Snigirev, A.

A. Simionovici, M. Chukalina, C. Schroer, M. Drakopoulos, A. Snigirev, I. Snigireva, B. Lengeler, K. Janssens, and F. Adams, “High-resolution x-ray fluorescence microtomography of homogeneous samples,” IEEE Trans. Nucl. Sci. 47(6), 2736–2740 (2000).
[Crossref]

Snigireva, I.

A. Simionovici, M. Chukalina, C. Schroer, M. Drakopoulos, A. Snigirev, I. Snigireva, B. Lengeler, K. Janssens, and F. Adams, “High-resolution x-ray fluorescence microtomography of homogeneous samples,” IEEE Trans. Nucl. Sci. 47(6), 2736–2740 (2000).
[Crossref]

Stout, D.

B. Dogdas, D. Stout, A. F. Chatziioannou, and R. M. Leahy, “Digimouse: a 3D whole body mouse atlas from CT and cryosection data,” Phys. Med. Biol. 52(3), 577–587 (2007).
[Crossref] [PubMed]

Su, H.

Sunaguchi, N.

T. Sasaya, N. Sunaguchi, K. Hyodo, T. Zeniya, and T. Yuasa, “Multi-pinhole fluorescent x-ray computed tomography for molecular imaging,” Sci. Rep. 7(1), 5742 (2017).
[Crossref] [PubMed]

Takeda, T.

T. Takeda, J. Wu, Q. Huo, T. Yuasa, K. Hyodo, F. A. Dilmanian, and T. Akatsuka, “X-ray fluorescent CT imaging of cerebral uptake of stable-iodine perfusion agent iodoamphetamine analog IMP in mice,” J. Synchrotron Radiat. 16(1), 57–62 (2009).
[Crossref] [PubMed]

Toprak, M. S.

J. C. Larsson, C. Vogt, W. Vågberg, M. S. Toprak, J. Dzieran, M. Arsenian-Henriksson, and H. M. Hertz, “High-spatial-resolution x-ray fluorescence tomography with spectrally matched nanoparticles,” Phys. Med. Biol. 63(16), 164001 (2018).
[Crossref] [PubMed]

Vågberg, W.

J. C. Larsson, C. Vogt, W. Vågberg, M. S. Toprak, J. Dzieran, M. Arsenian-Henriksson, and H. M. Hertz, “High-spatial-resolution x-ray fluorescence tomography with spectrally matched nanoparticles,” Phys. Med. Biol. 63(16), 164001 (2018).
[Crossref] [PubMed]

Vogt, C.

J. C. Larsson, C. Vogt, W. Vågberg, M. S. Toprak, J. Dzieran, M. Arsenian-Henriksson, and H. M. Hertz, “High-spatial-resolution x-ray fluorescence tomography with spectrally matched nanoparticles,” Phys. Med. Biol. 63(16), 164001 (2018).
[Crossref] [PubMed]

H. M. Hertz, J. C. Larsson, U. Lundström, D. H. Larsson, and C. Vogt, “Laboratory x-ray fluorescence tomography for high-resolution nanoparticle bio-imaging,” Opt. Lett. 39(9), 2790–2793 (2014).
[Crossref] [PubMed]

Wang, Y.

Y. Wang, T. R. Mazur, O. Green, Y. Hu, H. Li, V. Rodriguez, H. O. Wooten, D. Yang, T. Zhao, S. Mutic, and H. H. Li, “A GPU-accelerated Monte Carlo dose calculation platform and its application toward validating an MRI-guided radiation therapy beam model,” Med. Phys. 43(7), 4040–4052 (2016).
[Crossref] [PubMed]

Wild, S. M.

Wooten, H. O.

Y. Wang, T. R. Mazur, O. Green, Y. Hu, H. Li, V. Rodriguez, H. O. Wooten, D. Yang, T. Zhao, S. Mutic, and H. H. Li, “A GPU-accelerated Monte Carlo dose calculation platform and its application toward validating an MRI-guided radiation therapy beam model,” Med. Phys. 43(7), 4040–4052 (2016).
[Crossref] [PubMed]

Wu, J.

T. Takeda, J. Wu, Q. Huo, T. Yuasa, K. Hyodo, F. A. Dilmanian, and T. Akatsuka, “X-ray fluorescent CT imaging of cerebral uptake of stable-iodine perfusion agent iodoamphetamine analog IMP in mice,” J. Synchrotron Radiat. 16(1), 57–62 (2009).
[Crossref] [PubMed]

Xiao, T.

Q. Yang, B. Deng, G. Du, H. Xie, G. Zhou, T. Xiao, and H. Xu, “X‐ray fluorescence computed tomography with absorption correction for biomedical samples,” XRay Spectrom. 43(5), 278–285 (2014).
[Crossref]

Xie, H.

Q. Yang, B. Deng, G. Du, H. Xie, G. Zhou, T. Xiao, and H. Xu, “X‐ray fluorescence computed tomography with absorption correction for biomedical samples,” XRay Spectrom. 43(5), 278–285 (2014).
[Crossref]

Xing, L.

M. Ahmad, M. Bazalova-Carter, R. Fahrig, and L. Xing, “Optimized detector angular configuration increases the sensitivity of x-ray fluorescence computed tomography (XFCT),” IEEE Trans. Med. Imaging 34(5), 1140–1147 (2015).
[Crossref] [PubMed]

Y. Kuang, G. Pratx, M. Bazalova, B. Meng, J. Qian, and L. Xing, “First demonstration of multiplexed X-ray fluorescence computed tomography (XFCT) imaging,” IEEE Trans. Med. Imaging 32(2), 262–267 (2013).
[Crossref] [PubMed]

M. Bazalova, Y. Kuang, G. Pratx, and L. Xing, “Investigation of X-ray fluorescence computed tomography (XFCT) and K-edge imaging,” IEEE Trans. Med. Imaging 31(8), 1620–1627 (2012).
[Crossref] [PubMed]

Xu, H.

Q. Yang, B. Deng, G. Du, H. Xie, G. Zhou, T. Xiao, and H. Xu, “X‐ray fluorescence computed tomography with absorption correction for biomedical samples,” XRay Spectrom. 43(5), 278–285 (2014).
[Crossref]

Yang, D.

Y. Wang, T. R. Mazur, O. Green, Y. Hu, H. Li, V. Rodriguez, H. O. Wooten, D. Yang, T. Zhao, S. Mutic, and H. H. Li, “A GPU-accelerated Monte Carlo dose calculation platform and its application toward validating an MRI-guided radiation therapy beam model,” Med. Phys. 43(7), 4040–4052 (2016).
[Crossref] [PubMed]

Yang, Q.

Q. Yang, B. Deng, G. Du, H. Xie, G. Zhou, T. Xiao, and H. Xu, “X‐ray fluorescence computed tomography with absorption correction for biomedical samples,” XRay Spectrom. 43(5), 278–285 (2014).
[Crossref]

Yuasa, T.

T. Sasaya, N. Sunaguchi, K. Hyodo, T. Zeniya, and T. Yuasa, “Multi-pinhole fluorescent x-ray computed tomography for molecular imaging,” Sci. Rep. 7(1), 5742 (2017).
[Crossref] [PubMed]

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Figures (9)

Fig. 1
Fig. 1 (a) 3D rendering of the mouse phantom created from the DIGIMOUSE atlas with segmented organs. Colors are used to qualitatively distinguish different organs that are segmented in the atlas. The resolution in the phantom is 100 µm in all directions. (b) 3D rendered overlay of CT & XRF reconstructions of data from a typical XRF-GPU simulation. In this case, the lungs of the mouse were artificially “injected” with molybdenum NPs and then imaged using a simulation of our laboratory arrangement, see Sect. 2.2.2. The resolution in each axial slice is 200 µm while the slice separation is 500 µm. 3D visualizations were made using Amira 6.3 (FEI Visualization Sciences Group, Bordeaux France)
Fig. 2
Fig. 2 Schematic of the laboratory pencil-beam XRF tomography arrangement which is simulated using the XRF-GPU computational model. See [1] for details on the laboratory arrangement.
Fig. 3
Fig. 3 Tumor bearing mouse phantom with (a) 3-mm and (b) 1-mm diameter tumors (red) in the hip region. The lower features (purple) are the testis, and the top feature (green) is the bladder. The resolution in the phantom is 100 µm in all dimensions.
Fig. 4
Fig. 4 Demonstration of self-absorption and its correction on a simulated lung slice. (a) The lung in the mouse model was artificially “injected” with 2 mg/ml of molybdenum NPs. Qualitatively one can see large differences in the reconstructed slices with different levels of correction. (b) Quantitative comparisons using histograms over the whole 3D distributions of the lungs, for the different levels of correction.
Fig. 5
Fig. 5 (a) Quantitative accuracy estimates of lung reconstructions using the metrics presented in section 2.2.4. (b) Histogram for the lowest concentration of 0.1 mg/ml, exhibiting a different shape than histograms at higher concentrations (c.f., Fig. 4(b)).
Fig. 6
Fig. 6 3-mm diameter tumors with 2 mg/ml Mo NPs. Reconstructions were made without (a) and with (b) self-absorption correction. Top row shows overlay of 3D-rendered CT (grey) and XRF (red) reconstructions linearly interpolated for easier qualitative inspection. The threshold in the XRF reconstructions has been adjusted individually in (a) and (b) to suppress background noise in favor of the tumors. The top right box in each subfigure displays the mean value ( c mean ) in each reconstructed tumor and the relative mean error (RME) in parenthesis. The bottom row shows the corresponding tumor histograms.
Fig. 7
Fig. 7 3-mm diameter tumors with 0.1 mg/ml Mo NPs. Reconstructions were made without (a) and with (b) self-absorption correction. Top row shows overlay of 3D-rendered CT (grey) and XRF (red) reconstructions linearly interpolated for easier qualitative inspection. Due to the low concentration, completely separating the tumors from the background noise is not possible using a lower threshold on the XRF reconstruction. The threshold was therefore adjusted individually in (a) and (b) to balance the trade-off between visualizing tumors and background noise. Note that the threshold in (b) could be set higher without losing relevant tumor feature. The top right box in each subfigure displays the mean value ( c mean ) in each reconstructed tumor and the relative mean error (RME) in parenthesis. The bottom row shows the corresponding tumor histograms.
Fig. 8
Fig. 8 1-mm diameter tumors with 2 mg/ml Mo NPs. Reconstructions were made without (a) and with (b) self-absorption correction. Top row shows overlay of 3D-rendered CT (grey) and XRF (red) reconstructions linearly interpolated for easier qualitative inspection. The threshold in the XRF reconstruction has been adjusted individually in (a) and (b) to suppress background noise in favor of the tumors. The top right box in each subfigure displays the mean value ( c mean ) in each reconstructed tumor and the relative mean error (RME) in parenthesis. The bottom row shows the corresponding tumor histograms.
Fig. 9
Fig. 9 3D-rendered overlay of CT (grey) and XRF (red) reconstructions of sub-mm tumors artificially “injected” with 2 mg/ml Mo NPs. Self-absorption correction has been implemented. The inset zooms in on the left tumor, showing the sub-mm size in the reconstruction. Reconstructions are visualized with linear interpolation for facilitated qualitative inspection. The lower threshold in the XRF reconstruction is be set to completely distinguish the tumors from the background noise.

Tables (2)

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Table 1 Acquisition parameters for the XRF tomography simulations

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Table 2 Local SNR2 for reconstructed sub-mm tumors